Similarity measures for Atanassov’s intuitionistic fuzzy sets: some dilemmas and challenges

Q4 Engineering Control and Cybernetics Pub Date : 2022-06-01 DOI:10.2478/candc-2022-0016
E. Szmidt, J. Kacprzyk, Paweł Bujnowski
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引用次数: 1

Abstract

Abstract We discuss some aspects of similarity measures in the context of Atanassov’s intuitionistic fuzzy sets (IFSs, for short). IFSs, proposed in 1983, are a relatively new tool for the modeling and simulation and, because of their construction, present us with new challenges as far the similarity measures are concerned. Specifically, we claim that the distances alone are not a proper measure of similarity for the IFSs. We stress the role of a lack of knowledge concerning elements (options, decisions, etc.) and point out the role of the opposing (complementing) elements. We also pay attention to the fact that it is not justified to talk about similarity when one has not enough knowledge about the compared objects/elements. Some novel measures of similarity are presented.
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阿塔纳索夫直觉模糊集的相似测度:一些困境和挑战
摘要本文在Atanassov的直觉模糊集(ifs,简称ifs)的背景下讨论了相似性度量的一些方面。1983年提出的ifs是一种相对较新的建模和仿真工具,由于其结构,就相似性度量而言,它给我们带来了新的挑战。具体地说,我们认为距离本身并不是衡量ifs相似性的适当标准。我们强调缺乏有关要素(选择、决定等)的知识的作用,并指出相反(补充)要素的作用。我们还注意到,当一个人对被比较的对象/元素没有足够的了解时,谈论相似性是不合理的。提出了一些新的相似性度量方法。
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来源期刊
Control and Cybernetics
Control and Cybernetics 工程技术-计算机:控制论
CiteScore
0.50
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0.00%
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0
期刊介绍: The field of interest covers general concepts, theories, methods and techniques associated with analysis, modelling, control and management in various systems (e.g. technological, economic, ecological, social). The journal is particularly interested in results in the following areas of research: Systems and control theory: general systems theory, optimal cotrol, optimization theory, data analysis, learning, artificial intelligence, modelling & identification, game theory, multicriteria optimisation, decision and negotiation methods, soft approaches: stochastic and fuzzy methods, computer science,
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